numpy - Calculate jaccard distance using scipy in python -


i have 2 separate lists follows.

list1 =[[0.0, 0.75, 0.2], [0.0, 0.5, 0.7]] list2 =[[0.9, 0.0, 0.8], [0.0, 0.0, 0.8], [1.0, 0.0, 0.0]]

i want list1 x list2 jaccard distance matrix (i.e. matrix includes 6 values: 2 x 3)

    example; [0.0, 0.75, 0.2] in list1 3 lists in list2 [0.0, 0.5, 0.7] in list1 3 lists in list2

i tried both pdist , cdist. following errors respectively; typeerror: pdist() got multiple values argument 'metric' , valueerror: xa must 2-dimensional array..

please me fix issue.

you need pass pdist m x n 2d array. construct it, can use simple nested loop. :

import scipy.spatial.distance dist  list1 =[[0.0, 0.75, 0.2], [0.0, 0.5, 0.7]] list2 =[[0.9, 0.0, 0.8], [0.0, 0.0, 0.8], [1.0, 0.0, 0.0]] distance = [] elem1 in list1:     elem2 in list2:         distance.append(dist.pdist([elem1,elem2], 'jaccard'))

you results in distance array.


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